Data co-processing method and device for multi-element equipment of data center

Abstract

The invention relates to the technical field of data monitoring, and discloses a data co-processing method and device for multi-element equipment of a data center, and the method comprises the steps: obtaining hardware facility data, original monitoring data and central console data; performing quantity judgment according to the hardware facility data to obtain hardware synchronization data; correlation degree calculation is carried out according to the original monitoring data, and correlation monitoring parameters are obtained; performing priority division according to a preset threshold parameter and the original monitoring data to obtain priority data; carrying out progressive data migration according to the central console data and the priority data to obtain smooth migration data; performing control parameter calculation according to the hardware synchronization data, the associated monitoring parameters and the smooth migration data to obtain optimal monitoring parameters; and matching the optimal monitoring parameters with preset monitoring data to obtain an optimal monitoring mode so as to control the system to perform cross-regional monitoring. The method can adjust the monitoring strategy in time according to the actual operation state.

Description

Technical Field

[0001] The present invention relates to the technical field of data monitoring, and particularly to a data collaborative processing method and device for multi - device in a data center. Background Art

[0002] In the management of modern data centers, intelligent monitoring systems have become an important technical means to ensure the stable operation of the system. An intelligent monitoring system refers to the real - time collection and analysis of multi - dimensional data such as the hardware devices, network status, and application performance of a data center, so as to achieve comprehensive monitoring of the operating status of the data center and fault warning, and ensure the efficient operation of the data center.

[0003] Existing data center monitoring methods mainly compare and alarm the collected real - time data based on preset monitoring index thresholds. When the monitoring index exceeds the preset range, the system will trigger an alarm and notify the operation and maintenance personnel for processing. This monitoring method can only passively wait for the index to be abnormal before intervening.

[0004] This single - threshold monitoring method cannot adjust the monitoring strategy in a timely manner according to the actual operating status, resulting in insufficient flexibility of the monitoring system and being unable to effectively cope with sudden failures. Summary of the Invention

[0005] The present invention provides a data collaborative processing method and device for multi - device in a data center to solve the problem that the single - threshold monitoring method in the prior art cannot adjust the monitoring strategy in a timely manner according to the actual operating status, resulting in insufficient flexibility of the monitoring system and being unable to effectively cope with sudden failures.

[0006] In a first aspect, to solve the above - mentioned technical problem, the present invention provides a data collaborative processing method for multi - device in a data center, including:

[0007] Obtaining hardware facility data, original monitoring data, and central console data;

[0008] Performing quantity determination according to the hardware facility data to obtain hardware synchronization data;

[0009] Calculating the correlation degree according to the original monitoring data to obtain correlation monitoring parameters;

[0010] Performing priority division according to preset threshold parameters and the original monitoring data to obtain priority data;

[0011] Performing data progressive migration according to the central console data and the priority data to obtain smooth migration data;

[0012] Calculating control parameters according to the hardware synchronization data, the correlation monitoring parameters, and the smooth migration data to obtain optimal monitoring parameters;

[0013] Match the optimal monitoring parameters with the preset monitoring data to obtain the optimal monitoring mode for cross-regional monitoring of the control system.

[0014] In an alternative embodiment,

[0015] The hardware facility data includes the number of internal network devices, the number of external network devices, and the topology configuration number;

[0016] The original monitoring data includes CPU usage rate, memory occupancy rate, I / O port load number, and bandwidth usage number;

[0017] The central console data includes central first control data, central second control data, and central third control data.

[0018] In an alternative embodiment, the quantity determination based on the hardware facility data to obtain the hardware synchronization data includes:

[0019] Determine the one with the largest quantity in the hardware facility data as the first synchronization data;

[0020] Determine the one with the second largest quantity in the hardware facility data as the second synchronization data;

[0021] Determine the one with the third largest quantity in the hardware facility data as the third synchronization data;

[0022] The hardware synchronization data includes the first synchronization data, the second synchronization data, and the third synchronization data.

[0023] In an alternative embodiment, the calculation of the correlation degree based on the original monitoring data to obtain the correlation monitoring parameters includes:

[0024] The correlation monitoring parameters are calculated through the following formula:

[0025]

[0026] In the formula, P is the correlation monitoring parameter, d is the damping parameter, N is the bandwidth usage number, I is the I / O port load number, c is the CPU usage rate, m is the memory occupancy rate, and e is the base of the natural logarithm.

[0027] In an alternative embodiment, the priority division based on the preset threshold parameters and the original monitoring data to obtain the priority data includes:

[0028] Normalize the original monitoring data to obtain the normalized monitoring data;

[0029] Perform priority division on the normalized monitoring data:

[0030] When the normalized monitoring data is greater than or equal to the first threshold parameter, it is classified as first-priority data;

[0031] When the normalized monitoring data is greater than or equal to the second threshold parameter and less than the first threshold parameter, it is classified as second-priority data;

[0032] When the normalized monitoring data is greater than or equal to the third threshold parameter and less than the second threshold parameter, it is classified as third-priority data;

[0033] The normalized monitoring data includes normalized CPU usage rate, normalized memory occupancy rate, normalized I / O port load number, and normalized bandwidth usage number;

[0034] The priority data includes first-priority data, second-priority data, and third-priority data.

[0035] In an optional implementation manner, the data progressive migration according to the central console data and the priority data to obtain smooth migration data includes:

[0036] Migrate the first-priority data to the central first control data to obtain central first-priority data;

[0037] Migrate the second-priority data to the central second control data to obtain central second-priority data;

[0038] Migrate the third-priority data to the central third control data to obtain central third-priority data;

[0039] The smooth migration data includes central first-priority data, central second-priority data, and central third-priority data.

[0040] In an optional implementation manner, the control parameter calculation according to the hardware synchronization data, the associated monitoring parameters, and the smooth migration data to obtain the optimal monitoring parameters includes:

[0041] The optimal monitoring parameters are calculated through the following formula:

[0042] Q c = P × (αA + βB + γC) × e -P

[0043] In the formula, Q c is the optimal monitoring parameter, P is the associated monitoring parameter, α is the first synchronization data, β is the second synchronization data, γ is the third synchronization data, A is the central first-priority data, B is the central second-priority data, C is the central third-priority data, and e is the base of the natural logarithm.

[0044] In an alternative embodiment, the matching of the optimal monitoring parameters with the preset monitoring data to obtain the optimal monitoring mode for controlling the cross-region monitoring of the system includes:

[0045] When the optimal monitoring parameter is greater than or equal to the first control parameter threshold, the optimal monitoring mode is the preset first monitoring mode;

[0046] When the optimal monitoring parameter is greater than or equal to the second control parameter threshold and less than the first control parameter threshold, the optimal monitoring mode is the preset second monitoring mode;

[0047] When the optimal monitoring parameter is greater than or equal to the third control parameter threshold and less than the second control parameter threshold, the optimal monitoring mode is the preset third monitoring mode;

[0048] When the optimal monitoring parameter is greater than or equal to the fourth control parameter threshold and less than the third control parameter threshold, the optimal monitoring mode is the preset fourth monitoring mode;

[0049] When the optimal monitoring parameter is less than the fourth control parameter threshold, the optimal monitoring mode is the preset fifth monitoring mode.

[0050] In a second aspect, the present invention provides a data collaborative processing device for multi-element devices in a data center, including:

[0051] A data acquisition module for acquiring hardware facility data, original monitoring data, and central console data;

[0052] A synchronous data adjustment module for performing quantity determination based on the hardware facility data to obtain hardware synchronous data;

[0053] A correlation calculation module for calculating the correlation degree based on the original monitoring data to obtain correlation monitoring parameters;

[0054] A priority division module for performing priority division based on preset threshold parameters and the original monitoring data to obtain priority data;

[0055] A data progressive migration module for performing data progressive migration based on the central console data and the priority data to obtain smooth migration data;

[0056] A monitoring parameter calculation module for calculating control parameters based on the hardware synchronous data, the correlation monitoring parameters, and the smooth migration data to obtain optimal monitoring parameters;

[0057] A monitoring mode matching module for matching the optimal monitoring parameters with the preset monitoring data to obtain the optimal monitoring mode for controlling the cross-region monitoring of the system.

[0058] In a third aspect, the present invention further provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the data collaborative processing method for a multi-device in a data center described in any one of the above.

[0059] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the data collaborative processing method for a multi-device in a data center described in any one of the above.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] The present invention discloses a data collaborative processing method for a multi-device in a data center, including obtaining hardware facility data, original monitoring data, and central console data; performing quantity determination according to the hardware facility data to obtain hardware synchronization data; calculating the correlation degree according to the original monitoring data to obtain correlation monitoring parameters; performing priority division according to preset threshold parameters and the original monitoring data to obtain priority data; performing data progressive migration according to the central console data and the priority data to obtain smooth migration data; calculating control parameters according to the hardware synchronization data, the correlation monitoring parameters, and the smooth migration data to obtain optimal monitoring parameters; and matching the optimal monitoring parameters with preset monitoring data to obtain an optimal monitoring mode for controlling cross-region monitoring of the system. The method first obtains hardware facility data, original monitoring data, and central console data, performs quantity determination according to the hardware facility data to obtain hardware synchronization data, then calculates the correlation degree according to the original monitoring data to obtain correlation monitoring parameters, and then performs priority division according to preset threshold parameters and the original monitoring data to obtain priority data, so as to perform data progressive migration according to the central console data and the priority data to obtain smooth migration data, and finally calculates control parameters according to the hardware synchronization data, the correlation monitoring parameters, and the smooth migration data to obtain optimal monitoring parameters, and thus matches the optimal monitoring parameters with preset monitoring data to obtain an optimal monitoring mode for controlling cross-region monitoring of the system.

[0062] The method can adjust the monitoring strategy in a timely manner according to the actual operating state, improving the flexibility of the monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is a schematic flowchart of a data collaborative processing method for a multi-device in a data center provided by the first embodiment of the present invention;

[0064] Figure 2 It is a schematic structural diagram of a data collaborative processing device for multi - device in a data center provided by the second embodiment of the present invention. Detailed implementation manners

[0065] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0066] Referring to Figure 1 , the first embodiment of the present invention provides a data collaborative processing method for multi - device in a data center, including the following steps:

[0067] S11, obtaining hardware facility data, original monitoring data, and central console data;

[0068] S12, performing quantity determination according to the hardware facility data to obtain hardware synchronization data;

[0069] S13, calculating the correlation degree according to the original monitoring data to obtain correlation monitoring parameters;

[0070] S14, performing priority division according to the preset threshold parameters and the original monitoring data to obtain priority data;

[0071] S15, performing data progressive migration according to the central console data and the priority data to obtain smooth migration data;

[0072] S16, calculating control parameters according to the hardware synchronization data, the correlation monitoring parameters, and the smooth migration data to obtain optimal monitoring parameters;

[0073] S17, matching the optimal monitoring parameters with the preset monitoring data to obtain an optimal monitoring mode for controlling cross - regional monitoring of the system.

[0074] In step S11, hardware facility data, original monitoring data, and central console data are obtained.

[0075] The hardware facility data includes the number of internal network devices, the number of external network devices, and the topology configuration number;

[0076] The original monitoring data includes CPU usage rate, memory occupancy rate, I / O port load number, and bandwidth usage number;

[0077] The central console data includes central first control data, central second control data, and central third control data.

[0078] It should be noted that by using a network scanning tool (such as Nmap) to scan the internal network, information such as the device IP addresses and device types in the network can be identified, and thus the total number of internal network devices can be calculated. By using firewall logs or network traffic analysis tools, the number of devices connected to the external network can be counted. By using a network topology discovery tool (such as SolarWinds Network Topology Mapper) to scan the network, the network topology structure diagram can be obtained, and the number of different network topology configurations can be counted. By using the built-in monitoring tools of the system (such as the Performance Monitor of Windows and the top command of Linux) or third-party monitoring software (such as Zabbix), the usage of the CPU can be obtained. By using system tools (such as Windows Explorer and the free command of Linux) or monitoring software, the usage of memory can be obtained. By using system tools (such as Windows Resource Monitor and the iostat command of Linux) or monitoring software, the system I / O port load count can be counted. By using network monitoring tools (such as Wireshark and NetFlow) or the management interfaces of network devices (such as routers and switches), the network traffic can be monitored in real time, and the usage of bandwidth can be counted. The central console data can be obtained through the API interface or database query function of the central console.

[0079] Exemplarily, a network scanning tool is used to obtain the number of internal network devices. In the internal network of an enterprise, the Nmap tool is used to scan the internal network. By setting an appropriate scanning range (such as 192.168.1.0 / 24), Nmap can quickly identify all the devices in the network, including servers, workstations, printers, and network devices, etc. The scanning result shows that there are a total of 150 devices in the internal network, including 20 servers, 100 workstations, 10 printers, and 20 network devices. In this way, the number of internal network devices can be quickly obtained.

[0080] Exemplarily, in a data center, the firewall logs are analyzed to count the number of external network devices. The firewall logs record all the connection requests and data traffic information entering and leaving the network. By filtering out the internal device records communicating with external IP addresses, it is obtained that 30 external devices have communicated with the internal network in the past 24 hours. These external devices include the devices of remote office workers, servers, and the devices of external service providers. In this way, the number of external network devices can be quickly obtained.

[0081] Exemplarily, in the network environment of a large enterprise, the SolarWinds Network TopologyMapper tool is used to discover the network topology of the entire network. This tool automatically discovers devices in the network through protocols such as ICMP and SNMP and generates a detailed network topology map. By analyzing the topology map, there are 5 different topology configurations in the network, including star topology (used to connect local area networks of each department), ring topology (used to connect key server clusters), and hybrid topology (used to connect network devices on different floors).

[0082] Exemplarily, on the server of a data center, the Zabbix monitoring system is used to regularly collect the CPU usage rate. Zabbix installs an agent program on the server to monitor the CPU usage in real time and sends the data to the monitoring server. In this way, the information on the CPU usage rate can be obtained.

[0083] Exemplarily, in the server environment of an enterprise, the memory occupancy rate is obtained through the Zabbix monitoring system. Zabbix regularly collects memory usage data through the agent program and visualizes it on the monitoring dashboard. In this way, the memory usage situation can be obtained.

[0084] Exemplarily, on the database server of an enterprise, the iostat command is used to regularly monitor the load situation of the I / O port. By analyzing the output data of iostat, the I / O operation frequency of the database server reaches 100 I / O operations per second, and the load number of the I / O port can be obtained as 100.

[0085] Exemplarily, in the network environment of an enterprise, the NetFlow tool is used to monitor the network bandwidth usage in real time. NetFlow is a network traffic analysis tool that can record detailed information such as the source address, destination address, protocol type, and traffic size of network traffic. By configuring the NetFlow collector, the traffic data of network devices (such as routers and switches) is collected on the monitoring server. By analyzing these data, the network bandwidth usage of the enterprise network is 60, and the network bandwidth usage of the enterprise network during peak periods is 80.

[0086] Exemplarily, the central first control data, the central second control data, and the central third control data are a specific parameter. By matching the value of this parameter with the data in the preset system database, the central control data can be obtained. For example, when the parameter value of the central first control data is 80, the specific central control data obtained at this time has a CPU configured as Intel Xeon E5-2680 v4, a memory configured as 128GB, a hard disk configured as 2TB SSD, a port rate of 1000Mbps, a user number capacity of 500 people, and a maximum load number of I / O ports of 200.

[0087] In step S12, a quantity determination is made based on the hardware facility data to obtain hardware synchronization data.

[0088] Determine the one with the largest quantity in the hardware facility data as the first synchronization data;

[0089] Determine the one with the second largest quantity in the hardware facility data as the second synchronization data;

[0090] Determine the one with the third largest quantity in the hardware facility data as the third synchronization data;

[0091] The hardware synchronization data includes the first synchronization data, the second synchronization data, and the third synchronization data.

[0092] It should be noted that the hardware facility data includes internal network devices, external network devices, and topology configurations. By counting the number of internal network devices, the number of external network devices, and the number of topology configurations, and then making a determination based on the quantities of the three, the hardware synchronization data can be obtained, where the hardware synchronization data includes the first synchronization data, the second synchronization data, and the third synchronization data.

[0093] Exemplarily, the hardware facility data is collected by multiple sensors and monitoring modules distributed in the factory workshop, and it is statistically obtained that the number of internal network devices is 100, the number of external network devices is 34, and the number of topology configurations is 42, and thus a determination is made based on this result. Therefore, the number of internal network devices is the lowest (100), which is determined as the first synchronization data, the number of topology configurations is the second (42), which is determined as the second synchronization data, and the number of external network devices is the third (34), which is determined as the third synchronization data.

[0094] In step S13, a correlation calculation is made based on the original monitoring data to obtain a correlation monitoring parameter.

[0095] The correlation monitoring parameter is calculated through the following formula:

[0096]

[0097] Wherein, P is the associated monitoring parameter, d is the damping parameter, N is the number of bandwidth usages, I is the number of I / O port loads, c is the CPU usage rate, m is the memory occupancy rate, and e is the base of the natural logarithm.

[0098] It should be noted that the formula comprehensively reflects the operating state of the system through multiple indicators. The number of bandwidth usages can obtain the current system's bandwidth usage through a network monitoring tool, or determine it through a configuration file and system logs. The number of I / O port loads refers to the load situation of the input / output interfaces, which can be obtained through the monitoring tools of the operating system (such as the iostat command in Linux). The CPU usage rate refers to the busy degree of the CPU, which can be obtained through the monitoring tools of the operating system (such as the top or vmstat command in Linux). The memory occupancy rate refers to the proportion of the used memory in the total memory in the system. Similarly, the memory usage can be obtained through the monitoring tools of the operating system (such as the free command in Linux). The introduction of the damping parameter d is used to balance the weights of different indicators. By adjusting the value of d, adjustments can be made between the bandwidth usage and the I / O load. When the number of bandwidth usages in the system is large and the number of I / O port loads is small, the associated monitoring parameter can be made larger by increasing the damping parameter d. At this time, the weight of the number of bandwidth usages is low, and the weight of the number of I / O port loads is high. The exponential term e 2-(c+m) considers the non-linear effects of the CPU and memory occupancy rates. When the CPU and memory occupancy rates are relatively high, the value of this exponential term will decrease significantly, thereby reducing the overall associated monitoring parameter P, reflecting that the system is facing greater pressure. This non-linear adjustment mechanism enables the formula to more sensitively capture extreme changes in the system state. At the same time, e 2-(c+m) monotonically decreases as c + m increases, which is consistent with the fact that the system pressure increases as the CPU and memory occupancy rates increase. In addition, the constant 2 in the exponential term is a stability parameter. When c + m approaches 2, the value of the exponential term e 2-(c+m) is close to 1, indicating that under normal operating conditions, the impacts of the CPU and memory occupancy rates on the associated monitoring parameter P are relatively small. When c + m is significantly greater than 2, the value of the exponential term will decrease rapidly, reflecting that the system is in an overloaded state.

[0099] Exemplarily, when the damping parameter d = 0.5, the number of bandwidth usages N = 100, the number of I / O port loads I = 50, the CPU usage rate c = 0.7, and the memory occupancy rate m = 0.6, through the formula it is calculated that P = 2.028.

[0100] In step S14, priority division is performed according to the preset threshold parameter and the original monitoring data to obtain priority data.

[0101] Normalize the original monitoring data to obtain normalized monitoring data;

[0102] Divide the normalized monitoring data into different priorities:

[0103] When the normalized monitoring data is greater than or equal to the first threshold parameter, it is classified as first-priority data;

[0104] When the normalized monitoring data is greater than or equal to the second threshold parameter and less than the first threshold parameter, it is classified as second-priority data;

[0105] When the normalized monitoring data is greater than or equal to the third threshold parameter and less than the second threshold parameter, it is classified as third-priority data;

[0106] The normalized monitoring data includes normalized CPU usage rate, normalized memory occupancy rate, normalized I / O port load number, and normalized bandwidth usage number;

[0107] The priority data includes first-priority data, second-priority data, and third-priority data.

[0108] It should be noted that first, the original monitoring data is normalized to eliminate the dimensional differences and numerical range differences between different monitoring metrics. The purpose of normalization is to convert all monitoring data into a unified numerical interval, which is [0, 1].

[0109] Among them, the normalization formula is:

[0110]

[0111] In the formula, D is the normalized monitoring data, L is the original monitoring data, L min is the minimum value in the original monitoring data, L max is the maximum value in the original monitoring data.

[0112] Exemplarily, set the first threshold parameter to 0.8, the second threshold parameter to 0.4, and the third threshold parameter to 0. When the bandwidth usage number N = 120, the I / O port load number I = 80, the CPU usage rate c = 0.7, and the memory occupancy rate m = 0.6 in the original monitoring data, so the minimum value L min = 0.6, the maximum value L max = 120 in the original monitoring data. According to the formula Among the calculated normalized monitoring data, the normalized CPU usage rate c' = 0.08, the normalized memory occupancy rate m' = 0, the normalized I / O port load number I' = 0.665, and the normalized bandwidth usage number N' = 1. Therefore, the normalized CPU usage rate c' = 0.08 is greater than the third threshold parameter and less than the second threshold parameter, and is classified as third-priority data. The normalized memory occupancy rate m' = 0 is equal to the third threshold parameter and less than the second threshold parameter, and is classified as third-priority data. The normalized I / O port load number I' = 0.665 is greater than the second threshold parameter and less than the first threshold parameter, and is classified as second-priority data. When the normalized bandwidth usage number N' = 1 is greater than the first threshold parameter, it is classified as first-priority data.

[0113] In step S15, data progressive migration is performed according to the central console data and the priority data to obtain smooth migration data.

[0114] Migrate the first-priority data to the central first control data to obtain central first-priority data;

[0115] Migrate the second-priority data to the central second control data to obtain central second-priority data;

[0116] Migrate the third-priority data to the central third control data to obtain central third-priority data;

[0117] The smooth migration data includes central first-priority data, central second-priority data, and central third-priority data.

[0118] It should be noted that the central first-priority data, central second-priority data, and central third-priority data are obtained by migrating the priority data to the smooth migration data. During the migration process, when the number of one of the data in the priority data is only one, the central priority data is this data. When the number of one of the data in the priority data is multiple, the central priority data is the accumulated data of the same priority data to obtain the smooth migration data.

[0119] Exemplarily, when the third-priority data is the normalized CPU usage rate and the normalized memory occupancy rate, the second-priority data is the normalized I / O port load number, and the first-priority data is the normalized bandwidth usage number, so the value C of the central third-priority data = 0 + 0.08 = 0.08, the central second-priority data B = 0.665, and the central first-priority data A = 1.

[0120] In step S16, control parameter calculation is performed according to the hardware synchronization data, the associated monitoring parameters, and the smooth migration data to obtain the optimal monitoring parameters.

[0121] The optimal monitoring parameter is calculated by the following formula:

[0122] Q c = P × (αA + βB + γC) × e -P

[0123] In the formula, Q c is the optimal monitoring parameter, P is the associated monitoring parameter, α is the first synchronization data, β is the second synchronization data, γ is the third synchronization data, A is the central first priority data, B is the central second priority data, C is the central third priority data, and e is the base of the natural logarithm.

[0124] It should be noted that this formula uses multi-dimensional data, making the optimal monitoring parameter Q c able to make the calculation result more accurate. The part of αA + βB + γC in the formula quantifies the influence of data with different priorities on the optimal monitoring parameter through weight allocation. The introduction of the exponential term e -P has a regulatory effect, fully considering the non-linear influence of the associated monitoring parameter P on the system state. When the associated monitoring parameter P is large, it indicates that the system is at a relatively high level. At this time, the value of the exponential term e -P will decrease significantly, thereby reducing the value of the optimal monitoring parameter Q c . This non-linear adjustment mechanism enables the formula to more sensitively capture the changes in the system state, improving the accuracy and sensitivity of monitoring. At the same time, the exponential term e -P monotonically decreases as P increases, which is consistent with the situation where the system pressure increases as the associated monitoring parameter P increases, having smoothness. When P approaches 0, the value of the exponential term e -P is close to 1, indicating that the system is in an abnormal operating state, and the influence of the associated monitoring parameter P on the optimal monitoring parameter Q c is small. While when P is significantly greater than 0, the value of the exponential term will rapidly decrease, reflecting that the system is in an overloaded state. This enhances the non-linear characteristics of the formula and ensures the smoothness and monotonicity of the formula.

[0125] Exemplarily, when the associated monitoring parameter P = 2.028, the first synchronization data α = 100, the second synchronization data β = 42, the third synchronization data γ = 34, the central first priority data A = 1, the central second priority data B = 0.665, and the central third priority data C = 0.08, according to the formula Q c = P × (αA + βB + γC) × e -P the optimal monitoring parameter Q c is calculated to be 34.67.

[0126] In step S17, the optimal monitoring mode is obtained by matching the optimal monitoring parameter with the preset monitoring data to control the cross-regional monitoring of the system.

[0127] When the optimal monitoring parameter is greater than or equal to the first control parameter threshold, the optimal monitoring mode is the preset first monitoring mode;

[0128] When the optimal monitoring parameter is greater than or equal to the second control parameter threshold and less than the first control parameter threshold, the optimal monitoring mode is the preset second monitoring mode;

[0129] When the optimal monitoring parameter is greater than or equal to the third control parameter threshold and less than the second control parameter threshold, the optimal monitoring mode is the preset third monitoring mode;

[0130] When the optimal monitoring parameter is greater than or equal to the fourth control parameter threshold and less than the third control parameter threshold, the optimal monitoring mode is the preset fourth monitoring mode;

[0131] When the optimal monitoring parameter is less than the fourth control parameter threshold, the optimal monitoring mode is the preset fifth monitoring mode.

[0132] It should be noted that the monitoring scopes of each monitoring mode are different. For example, in a four-direction venue, the first monitoring mode is global non-blind-spot monitoring, and the second monitoring mode is to monitor only three of the directions. By adjusting the optimal monitoring parameter in real time to match different monitoring modes, the flexibility of the monitoring system can be improved.

[0133] Exemplarily, set the first control parameter threshold to 50, the second control parameter threshold to 35, the third control parameter threshold to 20, and the fourth control parameter threshold to 10. According to the optimal monitoring parameter Q c = 34.67. At this time, when the optimal monitoring parameter is greater than the fourth control parameter threshold and less than the third control parameter threshold, the optimal monitoring mode is the preset fourth monitoring mode, so as to control the cross-region monitoring of the system according to the fourth monitoring mode.

[0134] In summary, the present invention discloses a data collaborative processing method for multi - device in a data center, including obtaining hardware facility data, original monitoring data, and central console data; performing quantity determination according to the hardware facility data to obtain hardware synchronization data; calculating the correlation degree according to the original monitoring data to obtain correlation monitoring parameters; performing priority division according to preset threshold parameters and the original monitoring data to obtain priority data; performing data gradual migration according to the central console data and the priority data to obtain smooth migration data; calculating control parameters according to the hardware synchronization data, the correlation monitoring parameters, and the smooth migration data to obtain optimal monitoring parameters; and matching the optimal monitoring parameters with preset monitoring data to obtain an optimal monitoring mode for controlling cross - regional monitoring of the system. The method first obtains hardware facility data, original monitoring data, and central console data, performs quantity determination according to the hardware facility data to obtain hardware synchronization data, then calculates the correlation degree according to the original monitoring data to obtain correlation monitoring parameters, and then performs priority division according to preset threshold parameters and the original monitoring data to obtain priority data, thereby performing data gradual migration according to the central console data and the priority data to obtain smooth migration data, and finally calculates control parameters according to the hardware synchronization data, the correlation monitoring parameters, and the smooth migration data to obtain optimal monitoring parameters, and then matches the optimal monitoring parameters with preset monitoring data to obtain an optimal monitoring mode for controlling cross - regional monitoring of the system. The method can adjust the monitoring strategy in a timely manner according to the actual operating state, improving the flexibility of the monitoring system.

[0135] Referring to Figure 2 , the second embodiment of the present invention provides a data collaborative processing device for multi - device in a data center, including:

[0136] A data acquisition module, configured to obtain hardware facility data, original monitoring data, and central console data;

[0137] A synchronization data adjustment module, configured to perform quantity determination according to the hardware facility data to obtain hardware synchronization data;

[0138] A correlation degree calculation module, configured to calculate the correlation degree according to the original monitoring data to obtain correlation monitoring parameters;

[0139] A priority division module, configured to perform priority division according to preset threshold parameters and the original monitoring data to obtain priority data;

[0140] A data gradual migration module, configured to perform data gradual migration according to the central console data and the priority data to obtain smooth migration data;

[0141] A monitoring parameter calculation module, configured to calculate control parameters based on the hardware synchronization data, the associated monitoring parameters, and the smooth migration data to obtain optimal monitoring parameters;

[0142] A monitoring mode matching module, configured to match the optimal monitoring parameters with preset monitoring data to obtain an optimal monitoring mode for controlling cross-region monitoring of the system.

[0143] Preferably, the synchronization data adjustment module is specifically configured to perform quantity determination based on the hardware facility data to obtain hardware synchronization data.

[0144] Determine the one with the largest quantity in the hardware facility data as the first synchronization data;

[0145] Determine the one with the second largest quantity in the hardware facility data as the second synchronization data;

[0146] Determine the one with the third largest quantity in the hardware facility data as the third synchronization data;

[0147] The hardware synchronization data includes the first synchronization data, the second synchronization data, and the third synchronization data.

[0148] Preferably, the correlation calculation module is specifically configured to calculate correlation monitoring parameters based on the original monitoring data.

[0149] The correlation monitoring parameters are calculated through the following formula:

[0150]

[0151] In the formula, P is the correlation monitoring parameter, d is the damping parameter, N is the bandwidth usage number, I is the I / O port load number, c is the CPU usage rate, m is the memory occupancy rate, and e is the base of the natural logarithm.

[0152] Preferably, the priority division module is specifically configured to perform priority division based on preset threshold parameters and the original monitoring data to obtain priority data.

[0153] Normalize the original monitoring data to obtain normalized monitoring data;

[0154] Perform priority division on the normalized monitoring data:

[0155] When the normalized monitoring data is greater than or equal to the first threshold parameter, it is divided into first-priority data;

[0156] When the normalized monitoring data is greater than or equal to the second threshold parameter and less than the first threshold parameter, it is divided into second-priority data;

[0157] When the normalized monitoring data is greater than or equal to the third threshold parameter and less than the second threshold parameter, it is classified as third-priority data;

[0158] The normalized monitoring data includes normalized CPU usage rate, normalized memory occupancy rate, normalized I / O port load number, and normalized bandwidth usage number;

[0159] The priority data includes first-priority data, second-priority data, and third-priority data.

[0160] Preferably, the data progressive migration module is specifically configured to perform data progressive migration according to the central console data and the priority data to obtain smooth migration data.

[0161] Migrate the first-priority data to the central first control data to obtain central first-priority data;

[0162] Migrate the second-priority data to the central second control data to obtain central second-priority data;

[0163] Migrate the third-priority data to the central third control data to obtain central third-priority data;

[0164] The smooth migration data includes central first-priority data, central second-priority data, and central third-priority data.

[0165] Preferably, the monitoring parameter calculation module is specifically configured to calculate control parameters according to the hardware synchronization data, the associated monitoring parameters, and the smooth migration data to obtain optimal monitoring parameters.

[0166] The calculating the control parameters according to the hardware synchronization data, the associated monitoring parameters, and the smooth migration data to obtain optimal monitoring parameters includes:

[0167] The optimal monitoring parameters are calculated by the following formula:

[0168] Q c = P×(αA + βB + γC)×e -P

[0169] In the formula, Q c is the optimal monitoring parameter, P is the associated monitoring parameter, α is the first synchronization data, β is the second synchronization data, γ is the third synchronization data, A is the central first-priority data, B is the central second-priority data, C is the central third-priority data, and e is the base of the natural logarithm.

[0170] Preferably, the monitoring mode matching module is specifically configured to match the optimal monitoring parameters with preset monitoring data to obtain an optimal monitoring mode for cross-regional monitoring of the control system.

[0171] When the optimal monitoring parameter is greater than or equal to the first control parameter threshold, the optimal monitoring mode is the preset first monitoring mode;

[0172] When the optimal monitoring parameter is greater than or equal to the second control parameter threshold and less than the first control parameter threshold, the optimal monitoring mode is the preset second monitoring mode;

[0173] When the optimal monitoring parameter is greater than or equal to the third control parameter threshold and less than the second control parameter threshold, the optimal monitoring mode is the preset third monitoring mode;

[0174] When the optimal monitoring parameter is greater than or equal to the fourth control parameter threshold and less than the third control parameter threshold, the optimal monitoring mode is the preset fourth monitoring mode;

[0175] When the optimal monitoring parameter is less than the fourth control parameter threshold, the optimal monitoring mode is the preset fifth monitoring mode.

[0176] It should be noted that a data collaborative processing device for multi-element devices in a data center provided in an embodiment of the present invention is used to execute all process steps of a data collaborative processing method for multi-element devices in a data center in the above embodiment. The working principles and beneficial effects of the two correspond one by one, and thus will not be elaborated herein.

[0177] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data collaborative processing program for multi-element devices in a data center. When the processor executes the computer program, the steps in the above embodiments of the data collaborative processing method for multi-element devices in a data center are implemented, such as Figure 1 Step S11 shown. Alternatively, when the processor executes the computer program, the functions of each module / unit in the above device embodiments are implemented, such as the data acquisition module.

[0178] Exemplarily, the computer program can be divided into one or more modules / units. The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0179] The electronic device may be a computing device such as a desktop computer, notebook, palm computer, and smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device. It may include more or fewer components than the above, or combine some components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0180] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the electronic device and connects all parts of the entire electronic device through various interfaces and lines.

[0181] The memory can be used to store the computer program and / or module. The processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0182] Among them, if the modules / units integrated in the electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0183] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0184] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data collaborative processing method for multiple devices in a data center, characterized in that: include: Obtain hardware facility data, raw monitoring data, and central console data; Perform quantity determination based on the hardware facility data to obtain hardware synchronization data; Calculate the correlation degree according to the original monitoring data to obtain the correlation monitoring parameters; Prioritizing the original monitoring data according to preset threshold parameters to obtain priority data; Performing gradual data migration according to the central console data and the priority data to obtain smooth migration data; Calculate control parameters according to the hardware synchronization data, the associated monitoring parameters and the smooth migration data to obtain optimal monitoring parameters; The optimal monitoring parameters are matched with preset monitoring data to obtain an optimal monitoring mode to control cross-region monitoring of the system.

2. The data collaborative processing method of multiple devices in a data center according to claim 1, characterized in that: The hardware facility data includes the number of internal network devices, the number of external network devices and the number of topology configurations; The raw monitoring data includes CPU usage, memory occupancy, I / O port load and bandwidth usage; The central control console data includes central first control data, central second control data and central third control data.

3. The data collaborative processing method of multiple devices in a data center according to claim 1, characterized in that: The step of determining the quantity according to the hardware facility data to obtain hardware synchronization data includes: Determine the hardware facility data with the largest quantity as the first synchronization data; Determine the second largest amount of the hardware facility data as the second synchronization data; Determine the third largest number of the hardware facility data as the third synchronization data; The hardware synchronization data includes first synchronization data, second synchronization data and third synchronization data.

4. The data collaborative processing method of multiple devices in a data center according to claim 2, characterized in that: The calculating the correlation degree according to the original monitoring data to obtain the correlation monitoring parameters includes: The associated monitoring parameters are calculated using the following formula: Where P is the associated monitoring parameter, d is the damping parameter, N is the bandwidth usage, I is the I / O port load, c is the CPU usage, m is the memory usage, and e is the base of the natural logarithm.

5. The data collaborative processing method of multiple devices in a data center according to claim 1, characterized in that: The priority classification is performed according to the preset threshold parameter and the original monitoring data to obtain the priority data, including: Normalizing the original monitoring data to obtain normalized monitoring data; The normalized monitoring data is prioritized: When the normalized monitoring data is greater than or equal to a first threshold parameter, it is classified as first priority data; When the normalized monitoring data is greater than or equal to the second threshold parameter and less than the first threshold parameter, it is classified as second priority data; When the normalized monitoring data is greater than or equal to the third threshold parameter and less than the second threshold parameter, it is classified as third priority data; The normalized monitoring data includes normalized CPU usage, normalized memory occupancy, normalized I / O port load number and normalized bandwidth usage number; The priority data includes first priority data, second priority data and third priority data.

6. The data collaborative processing method of multiple devices in a data center according to claim 1, characterized in that: The step of performing gradual data migration according to the central control station data and the priority data to obtain smooth migration data includes: Migrating the first priority data to the central first control data to obtain central first priority data; Migrating the second priority data to the central second control data to obtain central second priority data; Migrating the third priority data to the central third control data to obtain central third priority data; The smooth migration data includes central first priority data, central second priority data and central third priority data.

7. The data collaborative processing method of multiple devices in a data center according to claim 1, characterized in that: The calculating of control parameters according to the hardware synchronization data, the associated monitoring parameters and the smooth migration data to obtain the optimal monitoring parameters includes: The optimal monitoring parameters are calculated by the following formula: Q c =P×(αA+βB+γC)×e -P In the formula, Q c is the optimal monitoring parameter, P is the associated monitoring parameter, α is the first synchronization data, β is the second synchronization data, γ is the third synchronization data, A is the central first priority data, B is the central second priority data, C is the central third priority data, and e is the base of the natural logarithm.

8. The data collaborative processing method of multiple devices in a data center according to claim 1, characterized in that: The matching of the optimal monitoring parameters with the preset monitoring data to obtain the optimal monitoring mode to control the cross-region monitoring of the system includes: When the optimal monitoring parameter is greater than or equal to the first control parameter threshold, the optimal monitoring mode is the preset first monitoring mode; When the optimal monitoring parameter is greater than or equal to the second control parameter threshold and less than the first control parameter threshold, the optimal monitoring mode is the preset second monitoring mode; When the optimal monitoring parameter is greater than or equal to the third control parameter threshold and less than the second control parameter threshold, the optimal monitoring mode is the preset third monitoring mode; When the optimal monitoring parameter is greater than or equal to the fourth control parameter threshold and less than the third control parameter threshold, the optimal monitoring mode is the preset fourth monitoring mode; When the optimal monitoring parameter is less than the fourth control parameter threshold, the optimal monitoring mode is the preset fifth monitoring mode.

9. A data collaborative processing device for multiple devices in a data center, characterized in that: include: Data acquisition module, used to obtain hardware facility data, original monitoring data and central console data; A synchronization data adjustment module, used for determining the quantity according to the hardware facility data to obtain hardware synchronization data; A correlation calculation module, used to perform correlation calculation based on the original monitoring data to obtain correlation monitoring parameters; A priority division module, used to perform priority division according to preset threshold parameters and the original monitoring data to obtain priority data; A data gradual migration module, used to perform gradual data migration according to the central console data and the priority data to obtain smooth migration data; A monitoring parameter calculation module, used to calculate control parameters according to the hardware synchronization data, the associated monitoring parameters and the smooth migration data to obtain optimal monitoring parameters; The monitoring mode matching module is used to match the optimal monitoring parameters with the preset monitoring data to obtain the optimal monitoring mode to control the cross-region monitoring of the system.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the data collaborative processing method for multiple devices in a data center as described in any one of claims 1 to 8.

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